RevOps Lab

Nicole Bradshaw spent six months as interim global sales leader at Eventbrite — and discovered how much she didn't know about her own job. She joins Janis to explain why RevOps teams know exactly what good selling looks like but never apply it to themselves, and why an order-taking RevOps function is the one most easily automated away.We cover:What changed when Nicole became her own stakeholderWhy execs only want your top two problems, not all of themBalancing business impact against time to impactUsing OKRs as a vehicle, not a solutionMatching operating cadence to stakeholder levelStrong opinions held loosely — showing up with a point of viewWhy order-taking RevOps is the easiest role to automateBridging strategy and execution as RevOps' unique positionMulti-threading inside your own companyPicking the right champion for the right initiativeObjection handling: anticipate, prepare, bring dataMaking people's lives easier before you need favorsFraming every ask as a win-winNicole Bradshaw on LinkedIn: https://www.linkedin.com/in/nkbradshaw/ PandaDoc: https://www.pandadoc.com Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/ Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.weflow.ai/ Join the RevOps Chat Community: https://www.weflow.ai/community Subscribe to the RevOps Letter: https://www.weflow.ai/revops-letterResource recommendation: Harvard Business Review — Nicole's habit of blocking 30 minutes every other week for professional development readingChapters: (00:00) Intro & welcome to Nicole (00:37) Nicole's path into RevOps (03:08) Six months leading sales (04:43) Becoming your own stakeholder (07:34) Impact vs. time to impact (09:28) OKRs and operating cadence (13:08) Showing up with agency (17:19) AI-proofing the RevOps role (18:26) Bridging strategy and execution (22:56) Multi-threading and champions

Dan Jiao, VP RevOps at Aircall, has now moved two companies from a global functional model to regional GMs — at Signifyd and again at Aircall. He joins Janis to explain what actually changes when four business units each own their P&L, and why RevOps becomes the glue that keeps one company from quietly becoming five.We cover:Global functional vs. regional GM model — and what stays centralizedThe revenue scale where a regional split starts to make senseRunning RevOps as a product org: the global go-to-market operating systemWho owns segmentation vs. who co-designs territoriesEmbedding regional business partners as de facto chiefs of staffOne global scoring model, multiple regional calibrationsWhy capacity ratios differ market by marketRevOps as the neutral referee in budget and headcount debatesHolding one global forecasting standard across four business unitsBuyer-verified exit criteria instead of rep-filled fieldsSharing best practices when there's no global head of salesDan Jiao on LinkedIn: https://www.linkedin.com/in/danjiao/Aircall: https://aircall.ioJanis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/WeFlow: https://www.weflow.ai/Join the RevOps Chat Community: https://www.weflow.ai/communitySubscribe to the RevOps Letter: https://www.weflow.ai/revops-letterBook recommendation: Switch: How to Change Things When Change Is Hard by Chip & Dan HeathChapters:(00:00) Intro & welcome to Dan(02:33) Dan's path from sales to RevOps(04:46) Global functional vs. regional GM(08:12) When a regional split makes sense(11:18) Who owns segmentation and territories(14:04) The hybrid model and regional business partners(17:43) Account prioritization and scoring(20:49) Capacity planning across regions(25:53) RevOps at the budget table(27:52) One global forecasting standard(30:31) Buyer-verified exit criteria(32:43) Sharing best practices without a global sales lead(34:50) Book recommendation & close

Mollie Bodensteiner, VP RevOps at ZoomInfo and returning guest, joins Janis to break down how AI is reshaping RevOps roles, team structures, and career paths. RevOps used to report on the business — now it builds and governs the systems that run it. Mollie shares what the go-to-market engineer role actually is, why career ladders are turning into portfolios, and why she stopped doing case studies in hiring.We cover:From reactive help desk to building and governing the systems that run the businessAI sprawl: why "give everyone access" now creates a governance problemWhat a go-to-market engineer really is — beyond the LinkedIn hypeAgent ops: monitoring drift, QA, and cost like DevOps monitors uptimeWhy it's a skill set, not a new job titleThe analyst shift from firefighting to proactive intelligenceWho should own AI: centralized, federated, or center of excellenceFlatter orgs, wider spans, and horizontal layers replacing functional silosCareer paths becoming portfolios instead of laddersWhy Mollie killed case studies in her hiring processSkill vs. will — advice for early-career operatorsMollie Bodensteiner on LinkedIn: https://www.linkedin.com/in/molliebodensteiner/ ZoomInfo: https://www.zoominfo.com Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/ Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/ WeFlow: https://www.weflow.ai/ Join the RevOps Chat Community: https://www.weflow.ai/community Subscribe to the RevOps Letter: https://www.weflow.ai/revops-letterBook recommendation: The Accountable Organization by John MarchicaChapters: (00:00) Intro & welcome back to Mollie (03:26) How RevOps jobs have changed (05:30) AI sprawl and the governance problem (08:43) The go-to-market engineer role (12:06) Agent ops and the digital workforce (16:25) What happens to the analyst (20:30) Who owns AI in the org? (24:19) Flatter teams, wider spans (28:20) Making RevOps impact measurable (31:43) Why case studies are dead in hiring (34:11) Skill vs. will: advice for operators (37:07) Book recommendation & close

Colin Gerber, VP RevOps & Strategy at Socure, breaks down 17 years of CPQ evolution — from Zuora's billing-wrapper days to today's consumption-ready, platform-agnostic tools. He shares how Socure pressure-tested its pricing in Excel for a year before building anything, and how a new entitlements object now ties CRM, CPQ, and billing into one source of truth.We cover:CPQ's three eras: Zuora → Salesforce CPQ → platform-agnostic tools like DealHubPressure-testing pricing in Excel before implementing CPQSocure's shift to à la carte, consumption-based pricing across 35 modulesCommon CPQ mistakes: over-complication and workflow bloatScaling SKUs across ~170 countriesRevOps' role: guardrails, deal desk, pre-deal margin modelingUsing AI to auto-generate Solution Readiness DocumentsA bi-directional entitlements object unifying CRM, CPQ & billingAutomated entitlement capture for PLG motionsA preview of consumption-based forecastingColin Gerber on LinkedIn: https://www.linkedin.com/in/colinsgerber/ Socure: https://www.socure.com Weflow: https://www.weflow.ai/ RevOps Chat Community: https://www.weflow.ai/community RevOps Letter: https://www.weflow.ai/revops-letter Janis on LinkedIn: https://www.linkedin.com/in/janiszech/ Philipp on LinkedIn: https://www.linkedin.com/in/philippstelzer/Book recommendation: Team of Teams: New Rules of Engagement for a Complex World by General Stanley McChrystalChapters: (00:00) Intro & welcome to Colin (01:51) Colin's background (04:23) The three phases of CPQ history (10:00) CPQ challenges with consumption-based pricing (14:12) Common CPQ implementation mistakes (19:03) The role of RevOps in CPQ (21:19) Using AI in CPQ (25:21) Where's the system of truth? (32:26) Direct vs. self-service/PLG (34:44) Teaser: consumption-based forecasting (36:44) Book recommendation & close

Markus Jaensch, Head of RevOps at Aiven, joins Janis and Philipp to unpack what consumption-based pricing actually means for a RevOps team running a $100M+ ARR business. Aiven — the open-source data platform behind managed Kafka, Postgres, OpenSearch, and ClickHouse — recently crossed $100M ARR, and Markus walks through how the forecasting model, comp design, and territory setup have evolved over 4.5 years to deal with the fundamental problem of consumption: a "win" doesn't equal revenue, and the next 12 months can swing either direction.We cover:Why consumption ARR is structurally harder to forecast than SaaS bookingsWhy Aiven moved away from pure ARR forecasting and back to bookings + finance-modeled ARRThe three forecast buckets: new business, add-on, and commit contractsUsing MEDDPICC with mandatory mutual success plans and CRM score as a red-flag signalT-shirt sizing (S/M/L/XL) with solution architects to anchor opportunity sizeThe two non-negotiables to close-won at Aiven: valid payment method + 3 consecutive days of consumptionWhy RevOps owns the close-won gate — and the tradeoff of being a controlling functionThe Farmer/Hunter evolution: from split, to mixed, to dedicated Inside Sales + named-territory Field repsComp design: bookings as a sanity metric, ARR as the paid metric, with new-ARR ramp-to-sizeStack: Salesforce + data warehouse, and why Aiven switched from spot ARR to a 30-day averageBuilding the process around the customer first, not internal convenienceMarkus Jaensch on LinkedIn: https://www.linkedin.com/in/markus-jaensch/Aiven: https://aiven.ioWeflow: https://www.weflow.aiRevOps Chat Community: https://www.weflow.ai/communityRevOps Letter: https://www.weflow.ai/revopsletterJanis on LinkedIn: https://www.linkedin.com/in/janiszechPhilipp on LinkedIn: https://www.linkedin.com/in/philippstelzerBook recommendation: The Café on the Edge of the World by John StreleckyChapters:(00:00:00) Intro & Welcome to Markus(00:02:14) Why Consumption ARR Is the Topic — and Aiven Crossing $100M(00:03:39) Why Consumption ARR Is So Hard to Forecast(00:08:30) Bookings vs. ARR Forecasting — and Aiven's Evolution(00:12:53) MEDDPICC, Mutual Success Plans & Deal Hygiene(00:16:21) T-Shirt Sizing with Solution Architects(00:18:01) The 3-Day Consumption Rule for Close-Won(00:22:28) Modeling the ARR Ramp After a Booking(00:26:27) The Farmer/Hunter Evolution & Inside Sales Motion(00:29:37) Comp Design: Bookings, ARR, and Ramp-to-Size(00:31:00) Stack: Salesforce, Data Warehouse & 30-Day Average ARR(00:34:34) Final Learnings on Consumption Forecasting(00:36:29) The Customer-First Principle(00:38:02) Book Recommendation & Close